Dissecting fluctuating selection: A unified population and quantitative genetics

One of the longstanding debates in evolutionary biology is the effect of fluctuating selection on genetic changes in populations. However, the extent to which these periodic forces influence organisms at both genomic and phenotypic levels remains unclear. Furthermore, despite the compelling evidence of fluctuating selection from recent studies, there is a disconnect between empirical findings and theoretical models concerning the underlying mechanisms due to the limited evidence regarding the scale and processes that generate stable genome-wide oscillations. This study aims to elucidate the genetic and ecological factors driving fluctuating selection and to identify the parameters that produce consistent oscillatory patterns in allele frequencies. To address these longstanding challenges, we developed a modeling framework integrating quantitative and population genetics to simulate a population under various selection regimes. Using SLiM, a forward evolution simulator, we varied genetic (heritability and genomic architecture) and ecological (selection pressure and season length) parameters. Unlike previous models focusing on selection acting directly on loci, our approach evaluates individual fitness based on the shift in the seasonal optimum relative to the mean phenotype. We also applied spectral analysis to detect periodicity, indicating cyclical selective environments. Our simulations shed light on conditions sustaining oscillations in allele frequencies over time. Spectral analysis successfully identifies the periodic patterns from allele frequency, even under highly complex selection regimes. Not only does our study clarify the conditions that yield persistent oscillatory behaviors, but these parameters are also relatively easy to predict from natural population, providing a possibility of empirically testing these models.